Climate scenarios are the set of conditions used to represent estimates of future greenhouse gas (GHG) concentrations in the atmosphere. Climate models then evaluate how these GHG concentrations affect future (projected) climate.
The data in ClimRR include model results from two future climate scenarios, called Representative Concentration Pathways (RCPs):
RCP4.5: in this scenario, human GHG emissions peak around 2040, then decline
RCP8.5: in this scenario, human GHG emissions continue to rise throughout the 21-st century
Each RCP is modeled over a mid-century period (2045—2054) and end-of-century-period (2081 to 2094). A historical period (1995—2004) is also modeled using GHG concentrations during this period.
Code
import matplotlib.pyplot as pltimport geopandas as gpdimport warningsimport cartopy.crs as ccrsimport matplotlib.pyplot as pltimport irisimport iris.plot as ipltimport iris.quickplot as qpltwarnings.filterwarnings('ignore')from pathlib import Pathfrom ClimRR import DATA_DIRplt.rcParams.update({'axes.facecolor': 'none','figure.facecolor': 'none','savefig.facecolor': 'none','savefig.format': 'svg','axes.edgecolor': 'none','axes.grid': True,'axes.labelcolor': '#666','axes.titlecolor': '#666','grid.color': '#666','text.color': '#666','grid.linestyle': '--','grid.linewidth': 0.5,'grid.alpha': 0.4,'xtick.color': 'none','ytick.color': 'none','xtick.labelcolor': '#666','legend.edgecolor': 'none','ytick.labelcolor': '#666','savefig.transparent': True,})
import cartopy.crs as ccrsimport matplotlib.pyplot as pltimport irisimport iris.analysis.cartographyimport iris.plot as ipltimport iris.quickplot as qplt# Load some test data.fname = iris.sample_data_path("rotated_pole.nc")air_pressure = iris.load_cube(fname)# Plot #1: Point plot showing data values & a colorbar# plt.figure()fig, ax = plt.subplots()points = qplt.points(air_pressure, c=air_pressure.data)cb = plt.colorbar(points, orientation="horizontal")cb.set_label(air_pressure.units)plt.gca().coastlines()# Plot #2: Contourf of the point based dataplt.figure()qplt.contourf(air_pressure, 15)plt.gca().coastlines()# iplt.show()# Plot #3: Contourf overlaid by coloured point dataplt.figure()qplt.contourf(air_pressure)iplt.points(air_pressure, c=air_pressure.data)plt.gca().coastlines()# For the purposes of this example, add some bounds to the latitude# and longitudeair_pressure.coord("grid_latitude").guess_bounds()air_pressure.coord("grid_longitude").guess_bounds()# Plot #4: Block plotplt.figure()# fig, ax = plt.subplots()plt.axes(projection=ccrs.PlateCarree())iplt.pcolormesh(air_pressure)plt.gca().stock_img()plt.gca().coastlines()
<cartopy.mpl.feature_artist.FeatureArtist at 0x289431390>
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fname = iris.sample_data_path("air_temp.pp")temperature = iris.load_cube(fname)fig, ax = plt.subplots()# Plot #1: contourf with axes longitude from -180 to 180#plt.figure(figsize=(8, 5))plt.subplot(121)qplt.contourf(temperature, 15)fig.gca().coastlines()# Plot #2: contourf with axes longitude from 0 to 360proj = ccrs.PlateCarree(central_longitude=-180.0)plt.subplot(122, projection=proj)qplt.contourf(temperature, 15)fig.gca().coastlines()
<cartopy.mpl.feature_artist.FeatureArtist at 0x289737990>
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fname = iris.sample_data_path("air_temp.pp")fig, ax = plt.subplots()# Load exactly one cube from the given file.temperature = iris.load_cube(fname)# We only want a small number of latitudes, so filter some out# using "extract".temperature = temperature.extract( iris.Constraint(latitude=lambda cell: 68<= cell <78))for cube in temperature.slices("longitude"):# Create a string label to identify this cube (i.e. latitude: value). cube_label ="latitude: %s"% cube.coord("latitude").points[0]# Plot the cube, and associate it with a label. qplt.plot(cube, label=cube_label)# Add the legend with 2 columns.ax.legend(ncol=2)# Put a grid on the plot.ax.grid(True)# Tell matplotlib not to extend the plot axes range to nicely# rounded numbers.plt.axis("tight")
This example demonstrates the use of a single 3D data cube with time, latitude and longitude dimensions to plot a temperature series for a single latitude coordinate, with an inset plot of the data region.
Code
import numpy as npcube1 = iris.load_cube(iris.sample_data_path("ostia_monthly.nc"))# Slice into cube to retrieve data for the inset map showing the# data regionregion = cube1[-1, :, :]# Average over latitude to reduce cube to 1 dimensionplot_line = region.collapsed("latitude", iris.analysis.MEAN)# Open a window for plottingfig = plt.figure()# Add a single subplot (axes). Could also use "ax_main = plt.subplot()"ax_main = fig.add_subplot(1, 1, 1)# Produce a quick plot of the 1D cubeqplt.plot(plot_line)# Set x limits to match the dataax_main.set_xlim(0, plot_line.coord("longitude").points.max())# Adjust the y limits so that the inset map won't clash with main plotax_main.set_ylim(294, 310)ax_main.set_title("Meridional Mean Temperature")# Add grid linesax_main.grid()# Add a second set of axes specifying the fractional coordinates within# the figure with bottom left corner at x=0.55, y=0.58 with width# 0.3 and height 0.25.# Also specify the projectionax_sub = fig.add_axes( [0.55, 0.58, 0.3, 0.25], projection=ccrs.Mollweide(central_longitude=180),)# Use iris.plot (iplt) here so colour bar properties can be specified# Also use a sequential colour scheme to reduce confusion for those with# colour-blindnessiplt.pcolormesh(region, cmap="Blues")# Manually set the orientation and tick marks on your colour barticklist = np.linspace(np.min(region.data), np.max(region.data), 4)plt.colorbar(orientation="horizontal", ticks=ticklist)ax_sub.set_title("Data Region")# Add coastlinesax_sub.coastlines()# request to show entire map, using the colour mesh on the data region onlyax_sub.set_global()
Code
import irisimport numpy as npimport iris.plot as ipltimport iris.quickplot as qpltfrom iris.analysis import MEANfrom mpl_toolkits.axes_grid1 import make_axes_locatable# Loads air_temp.pp and "collapses" longitude into a single, average value.fname = iris.sample_data_path("air_temp.pp")temperature = iris.load_cube(fname)collapsed_temp = temperature.collapsed("longitude", MEAN)# Set y-axes with -90 and 90 limits and steps of 15 per tick.start, stop, step =-90, 90, 15yticks = np.arange(start, stop + step, step)ylim = [start, stop]# Plot "temperature" on a cartographic plot and set the ticks and titles# on the axes.fig = plt.figure(figsize=[12, 8])#fig, ax = plt.subplots()ax1 = fig.add_subplot(111, projection=ccrs.PlateCarree())im = iplt.contourf(temperature, cmap="RdYlBu_r")ax1.coastlines()ax1.gridlines()ax1.set_xticks([-180, -90, 0, 90, 180])ax1.set_yticks(yticks)ax1.set_title("Air Temperature")ax1.set_ylabel(f"Latitude / {temperature.coord('latitude').units}")ax1.set_xlabel(f"Longitude / {temperature.coord('longitude').units}")ax1.set_ylim(*ylim)# Create a Matplotlib AxesDivider object to allow alignment of other# Axes objects.divider = make_axes_locatable(ax1)# Gives the air temperature bar size, colour and a title.ax2 = divider.new_vertical( size="5%", pad=0.5, axes_class=plt.Axes, pack_start=True) # creates 2nd axisfig.add_axes(ax2)cbar = plt.colorbar( im, cax=ax2, orientation="horizontal") # puts colour bar on second axiscbar.ax.set_xlabel(f"{temperature.units}") # labels colour bar# Plot "collapsed_temp" on the mean graph and set the ticks and titles# on the axes.ax3 = divider.new_horizontal( size="30%", pad=0.4, axes_class=plt.Axes) # create 3rd axisfig.add_axes(ax3)qplt.plot( collapsed_temp, collapsed_temp.coord("latitude")) # plots temperature collapsed over longitude against latitudeax3.axhline(0, color="k", linewidth=0.5)# Creates zonal mean detailsax3.set_title("Zonal Mean")ax3.yaxis.set_label_position("right")ax3.yaxis.tick_right()ax3.set_yticks(yticks)ax3.grid()# Round each tick for the third ax to the nearest 20 (ready for use).data_max = collapsed_temp.data.max()x_max = data_max - data_max %-20data_min = collapsed_temp.data.min()x_min = data_min - data_min %20ax3.set_xlim(x_min, x_max)ax3.set_ylim(*ylim)plt.tight_layout()
Code
fname = iris.sample_data_path("air_temp.pp")# Load exactly one cube from the given file.temperature = iris.load_cube(fname)# We only want a small number of latitudes, so filter some out# using "extract".temperature = temperature.extract( iris.Constraint(latitude=lambda cell: 68<= cell <78))fig, ax = plt.subplots()for cube in temperature.slices("longitude"):# Create a string label to identify this cube (i.e. latitude: value). cube_label ="latitude: %s"% cube.coord("latitude").points[0]# Plot the cube, and associate it with a label. qplt.plot(cube, label=cube_label)# Add the legend with 2 columns.ax.legend(ncol=2)# Put a grid on the plot.ax.grid(True)# Tell matplotlib not to extend the plot axes range to nicely# rounded numbers.plt.axis("tight")
import iris.coord_categorisationimport matplotlib.colors as mcolsfig, ax = plt.subplots()# Load a sample air temperatures sequence.file_path = iris.sample_data_path("E1_north_america.nc")temperatures = iris.load_cube(file_path)# Create a year-number coordinate from the time information.iris.coord_categorisation.add_year(temperatures, "time")# Create a sample anomaly field for one chosen year, by extracting that# year and subtracting the time mean.sample_year =1982year_temperature = temperatures.extract(iris.Constraint(year=sample_year))time_mean = temperatures.collapsed("time", iris.analysis.MEAN)anomaly = year_temperature - time_mean# Construct a plot title string explaining which years are involved.years = temperatures.coord("year").pointsplot_title ="Temperature anomaly"plot_title +="\n{} differences from {}-{} average.".format( sample_year, years[0], years[-1])# Define scaling levels for the logarithmic colouring.minimum_log_level =0.1maximum_scale_level =3.0# Use a standard colour map which varies blue-white-red.# For suitable options, see the 'Diverging colormaps' section in:# http://matplotlib.org/stable/gallery/color/colormap_reference.htmlanom_cmap ="bwr"# Create a 'logarithmic' data normalization.anom_norm = mcols.SymLogNorm( linthresh=minimum_log_level, linscale=0.01, vmin=-maximum_scale_level, vmax=maximum_scale_level,)# Setting "linthresh=minimum_log_level" makes its non-logarithmic# data range equal to our 'zero band'.# Setting "linscale=0.01" maps the whole zero band to the middle colour value# (i.e., 0.5), which is the neutral point of a "diverging" style colormap.# Create an Axes, specifying the map projection.plt.axes(projection=ccrs.LambertConformal())# Make a pseudocolour plot using this colour scheme.mesh = iplt.pcolormesh(anomaly, cmap=anom_cmap, norm=anom_norm)# Add a colourbar, with extensions to show handling of out-of-range values.bar = plt.colorbar(mesh, orientation="horizontal", extend="both")# Set some suitable fixed "logarithmic" colourbar tick positions.tick_levels = [-3, -1, -0.3, 0.0, 0.3, 1, 3]bar.set_ticks(tick_levels)# Modify the tick labels so that the centre one shows "+/-<minumum-level>".tick_levels[3] =r"$\pm${:g}".format(minimum_log_level)bar.set_ticklabels(tick_levels)# Label the colourbar to show the units.bar.set_label("[{}, log scale]".format(anomaly.units))# Add coastlines and a title.plt.gca().coastlines()plt.title(plot_title)
Text(0.5, 1.0, 'Temperature anomaly\n1982 differences from 1860-2099 average.')
Source Code
---title: Climate Analysis with ClimRRjupyter: climrrdate: 2023-07-07---# Climate Analysis with ClimRR## Climate ScenariosClimate scenarios are the set of conditions used to represent estimates offuture greenhouse gas (GHG) concentrations in the atmosphere. Climate modelsthen evaluate how these GHG concentrations affect future (projected) climate.The data in ClimRR include model results from two future climate scenarios,called Representative Concentration Pathways (RCPs):1. **RCP4.5**: in this scenario, human GHG emissions peak around 2040, then decline2. **RCP8.5**: in this scenario, human GHG emissions continue to rise throughout the 21-st centuryEach RCP is modeled over a **mid-century period** (2045—2054) and**end-of-century-period** (2081 to 2094). A **historical period** (1995—2004)is also modeled using GHG concentrations during this period.```{python}import matplotlib.pyplot as pltimport geopandas as gpdimport warningsimport cartopy.crs as ccrsimport matplotlib.pyplot as pltimport irisimport iris.plot as ipltimport iris.quickplot as qpltwarnings.filterwarnings('ignore')from pathlib import Pathfrom ClimRR import DATA_DIRplt.rcParams.update({'axes.facecolor': 'none','figure.facecolor': 'none','savefig.facecolor': 'none','savefig.format': 'svg','axes.edgecolor': 'none','axes.grid': True,'axes.labelcolor': '#666','axes.titlecolor': '#666','grid.color': '#666','text.color': '#666','grid.linestyle': '--','grid.linewidth': 0.5,'grid.alpha': 0.4,'xtick.color': 'none','ytick.color': 'none','xtick.labelcolor': '#666','legend.edgecolor': 'none','ytick.labelcolor': '#666','savefig.transparent': True,})``````{python}shape = gpd.read_file( DATA_DIR.joinpath("GridCellsShapefile/GridCells.shp"))print(f"shape: {shape}")``````{python}import cartopy.crs as ccrsimport matplotlib.pyplot as pltimport irisimport iris.analysis.cartographyimport iris.plot as ipltimport iris.quickplot as qplt# Load some test data.fname = iris.sample_data_path("rotated_pole.nc")air_pressure = iris.load_cube(fname)# Plot #1: Point plot showing data values & a colorbar# plt.figure()fig, ax = plt.subplots()points = qplt.points(air_pressure, c=air_pressure.data)cb = plt.colorbar(points, orientation="horizontal")cb.set_label(air_pressure.units)plt.gca().coastlines()# Plot #2: Contourf of the point based dataplt.figure()qplt.contourf(air_pressure, 15)plt.gca().coastlines()# iplt.show()# Plot #3: Contourf overlaid by coloured point dataplt.figure()qplt.contourf(air_pressure)iplt.points(air_pressure, c=air_pressure.data)plt.gca().coastlines()# For the purposes of this example, add some bounds to the latitude# and longitudeair_pressure.coord("grid_latitude").guess_bounds()air_pressure.coord("grid_longitude").guess_bounds()# Plot #4: Block plotplt.figure()# fig, ax = plt.subplots()plt.axes(projection=ccrs.PlateCarree())iplt.pcolormesh(air_pressure)plt.gca().stock_img()plt.gca().coastlines()``````{python}fname = iris.sample_data_path("air_temp.pp")temperature = iris.load_cube(fname)fig, ax = plt.subplots()# Plot #1: contourf with axes longitude from -180 to 180#plt.figure(figsize=(8, 5))plt.subplot(121)qplt.contourf(temperature, 15)fig.gca().coastlines()# Plot #2: contourf with axes longitude from 0 to 360proj = ccrs.PlateCarree(central_longitude=-180.0)plt.subplot(122, projection=proj)qplt.contourf(temperature, 15)fig.gca().coastlines()``````{python}fname = iris.sample_data_path("air_temp.pp")fig, ax = plt.subplots()# Load exactly one cube from the given file.temperature = iris.load_cube(fname)# We only want a small number of latitudes, so filter some out# using "extract".temperature = temperature.extract( iris.Constraint(latitude=lambda cell: 68<= cell <78))for cube in temperature.slices("longitude"):# Create a string label to identify this cube (i.e. latitude: value). cube_label ="latitude: %s"% cube.coord("latitude").points[0]# Plot the cube, and associate it with a label. qplt.plot(cube, label=cube_label)# Add the legend with 2 columns.ax.legend(ncol=2)# Put a grid on the plot.ax.grid(True)# Tell matplotlib not to extend the plot axes range to nicely# rounded numbers.plt.axis("tight")```## Test Data Showing Inset PlotsThis example demonstrates the use of a single 3D data cube with time, latitudeand longitude dimensions to plot a temperature series for a single latitudecoordinate, with an inset plot of the data region.```{python}import numpy as npcube1 = iris.load_cube(iris.sample_data_path("ostia_monthly.nc"))# Slice into cube to retrieve data for the inset map showing the# data regionregion = cube1[-1, :, :]# Average over latitude to reduce cube to 1 dimensionplot_line = region.collapsed("latitude", iris.analysis.MEAN)# Open a window for plottingfig = plt.figure()# Add a single subplot (axes). Could also use "ax_main = plt.subplot()"ax_main = fig.add_subplot(1, 1, 1)# Produce a quick plot of the 1D cubeqplt.plot(plot_line)# Set x limits to match the dataax_main.set_xlim(0, plot_line.coord("longitude").points.max())# Adjust the y limits so that the inset map won't clash with main plotax_main.set_ylim(294, 310)ax_main.set_title("Meridional Mean Temperature")# Add grid linesax_main.grid()# Add a second set of axes specifying the fractional coordinates within# the figure with bottom left corner at x=0.55, y=0.58 with width# 0.3 and height 0.25.# Also specify the projectionax_sub = fig.add_axes( [0.55, 0.58, 0.3, 0.25], projection=ccrs.Mollweide(central_longitude=180),)# Use iris.plot (iplt) here so colour bar properties can be specified# Also use a sequential colour scheme to reduce confusion for those with# colour-blindnessiplt.pcolormesh(region, cmap="Blues")# Manually set the orientation and tick marks on your colour barticklist = np.linspace(np.min(region.data), np.max(region.data), 4)plt.colorbar(orientation="horizontal", ticks=ticklist)ax_sub.set_title("Data Region")# Add coastlinesax_sub.coastlines()# request to show entire map, using the colour mesh on the data region onlyax_sub.set_global()``````{python}import irisimport numpy as npimport iris.plot as ipltimport iris.quickplot as qpltfrom iris.analysis import MEANfrom mpl_toolkits.axes_grid1 import make_axes_locatable# Loads air_temp.pp and "collapses" longitude into a single, average value.fname = iris.sample_data_path("air_temp.pp")temperature = iris.load_cube(fname)collapsed_temp = temperature.collapsed("longitude", MEAN)# Set y-axes with -90 and 90 limits and steps of 15 per tick.start, stop, step =-90, 90, 15yticks = np.arange(start, stop + step, step)ylim = [start, stop]# Plot "temperature" on a cartographic plot and set the ticks and titles# on the axes.fig = plt.figure(figsize=[12, 8])#fig, ax = plt.subplots()ax1 = fig.add_subplot(111, projection=ccrs.PlateCarree())im = iplt.contourf(temperature, cmap="RdYlBu_r")ax1.coastlines()ax1.gridlines()ax1.set_xticks([-180, -90, 0, 90, 180])ax1.set_yticks(yticks)ax1.set_title("Air Temperature")ax1.set_ylabel(f"Latitude / {temperature.coord('latitude').units}")ax1.set_xlabel(f"Longitude / {temperature.coord('longitude').units}")ax1.set_ylim(*ylim)# Create a Matplotlib AxesDivider object to allow alignment of other# Axes objects.divider = make_axes_locatable(ax1)# Gives the air temperature bar size, colour and a title.ax2 = divider.new_vertical( size="5%", pad=0.5, axes_class=plt.Axes, pack_start=True) # creates 2nd axisfig.add_axes(ax2)cbar = plt.colorbar( im, cax=ax2, orientation="horizontal") # puts colour bar on second axiscbar.ax.set_xlabel(f"{temperature.units}") # labels colour bar# Plot "collapsed_temp" on the mean graph and set the ticks and titles# on the axes.ax3 = divider.new_horizontal( size="30%", pad=0.4, axes_class=plt.Axes) # create 3rd axisfig.add_axes(ax3)qplt.plot( collapsed_temp, collapsed_temp.coord("latitude")) # plots temperature collapsed over longitude against latitudeax3.axhline(0, color="k", linewidth=0.5)# Creates zonal mean detailsax3.set_title("Zonal Mean")ax3.yaxis.set_label_position("right")ax3.yaxis.tick_right()ax3.set_yticks(yticks)ax3.grid()# Round each tick for the third ax to the nearest 20 (ready for use).data_max = collapsed_temp.data.max()x_max = data_max - data_max %-20data_min = collapsed_temp.data.min()x_min = data_min - data_min %20ax3.set_xlim(x_min, x_max)ax3.set_ylim(*ylim)plt.tight_layout()``````{python}fname = iris.sample_data_path("air_temp.pp")# Load exactly one cube from the given file.temperature = iris.load_cube(fname)# We only want a small number of latitudes, so filter some out# using "extract".temperature = temperature.extract( iris.Constraint(latitude=lambda cell: 68<= cell <78))fig, ax = plt.subplots()for cube in temperature.slices("longitude"):# Create a string label to identify this cube (i.e. latitude: value). cube_label ="latitude: %s"% cube.coord("latitude").points[0]# Plot the cube, and associate it with a label. qplt.plot(cube, label=cube_label)# Add the legend with 2 columns.ax.legend(ncol=2)# Put a grid on the plot.ax.grid(True)# Tell matplotlib not to extend the plot axes range to nicely# rounded numbers.plt.axis("tight")``````{python}import iris.coord_categorisationimport matplotlib.colors as mcolsfig, ax = plt.subplots()# Load a sample air temperatures sequence.file_path = iris.sample_data_path("E1_north_america.nc")temperatures = iris.load_cube(file_path)# Create a year-number coordinate from the time information.iris.coord_categorisation.add_year(temperatures, "time")# Create a sample anomaly field for one chosen year, by extracting that# year and subtracting the time mean.sample_year =1982year_temperature = temperatures.extract(iris.Constraint(year=sample_year))time_mean = temperatures.collapsed("time", iris.analysis.MEAN)anomaly = year_temperature - time_mean# Construct a plot title string explaining which years are involved.years = temperatures.coord("year").pointsplot_title ="Temperature anomaly"plot_title +="\n{} differences from {}-{} average.".format( sample_year, years[0], years[-1])# Define scaling levels for the logarithmic colouring.minimum_log_level =0.1maximum_scale_level =3.0# Use a standard colour map which varies blue-white-red.# For suitable options, see the 'Diverging colormaps' section in:# http://matplotlib.org/stable/gallery/color/colormap_reference.htmlanom_cmap ="bwr"# Create a 'logarithmic' data normalization.anom_norm = mcols.SymLogNorm( linthresh=minimum_log_level, linscale=0.01, vmin=-maximum_scale_level, vmax=maximum_scale_level,)# Setting "linthresh=minimum_log_level" makes its non-logarithmic# data range equal to our 'zero band'.# Setting "linscale=0.01" maps the whole zero band to the middle colour value# (i.e., 0.5), which is the neutral point of a "diverging" style colormap.# Create an Axes, specifying the map projection.plt.axes(projection=ccrs.LambertConformal())# Make a pseudocolour plot using this colour scheme.mesh = iplt.pcolormesh(anomaly, cmap=anom_cmap, norm=anom_norm)# Add a colourbar, with extensions to show handling of out-of-range values.bar = plt.colorbar(mesh, orientation="horizontal", extend="both")# Set some suitable fixed "logarithmic" colourbar tick positions.tick_levels = [-3, -1, -0.3, 0.0, 0.3, 1, 3]bar.set_ticks(tick_levels)# Modify the tick labels so that the centre one shows "+/-<minumum-level>".tick_levels[3] =r"$\pm${:g}".format(minimum_log_level)bar.set_ticklabels(tick_levels)# Label the colourbar to show the units.bar.set_label("[{}, log scale]".format(anomaly.units))# Add coastlines and a title.plt.gca().coastlines()plt.title(plot_title)```
